Libraries I may use called
Registered S3 methods overwritten by 'htmltools':
method from
print.html tools:rstudio
print.shiny.tag tools:rstudio
print.shiny.tag.list tools:rstudio
Registered S3 method overwritten by 'rmarkdown':
method from
print.paged_df
library(tidyverse)
# install for visualizations
library(ggplot2)
# install to combine date and time
library(lubridate)
# for melting a df
library(reshape)
Reading in the first dataset, perceived health status.
perceived_health_status <- read_csv("../data/perceived_health_status.csv")
Inspecting the data.
perceived_health_status
Filtering.
perceived_health_status_stripped <- perceived_health_status %>%
filter(TIME_PERIOD == 2022) %>%
filter(REF_AREA == "AUT") %>%
filter(Sex == "Total") %>%
filter(Age == "15 years or over")
perceived_health_status_stripped
NA
As it appears that after 2007, the number of observations are more
significant in number, I will limit my data to 2007 and later. But,
since it appears the number of observations drops off in 2024, I will
limit my data to a range of 2007-2023. As well, I want to capture all
genders and ages.
# input code to limit year range, age range, and gender in perceived health status dataset
phs <- perceived_health_status %>%
filter(TIME_PERIOD == c(2007:2023)) %>%
filter(Sex == "Total") #%>%
# filter(Age == "15 years or over")
phs
Inspect the dataset for the number of years it covers.
barplot(table(perceived_health_status$TIME_PERIOD), main = "number of observations of year in the data")

Inspecting the data for balance in the health status column.
phs %>%
group_by(HEALTH_STATUS) %>%
summarize(n=n())
NA
Which countries are most heavily represented in the data? Denmark was
selected to be included I will download a Denmark only data and
investigate why it is no longer located in this data.
phs %>%
group_by(`Reference area`) %>%
summarize(n=n())
NA
phs %>%
group_by(AGE) %>%
summarize(n=n())
NA
# education_level <- read_csv("../data/educational_attainment_distribution_age_gender.csv")
# education_levels_defined <- read_csv("../data/educational_attainment_distribution.csv")
education_levels_three <- read_csv("../data/educational_attainment.csv")
# ISCED11A_5T8 = Tertiary education
# ISCED11A_3_4 = Upper secondary or post-secondary non-tertiary education
# ISCED11A_0T2 = Below upper secondary education
education_levels_three
NA
Verify that there are simply three categories for the education level
attained and Education attainment level columns
sort(unique(education_levels_three$ATTAINMENT_LEV))
[1] "ISCED11A_0T2" "ISCED11A_3_4" "ISCED11A_5T8"
sort(unique(education_levels_three$`Educational attainment level`))
[1] "Below upper secondary education" "Tertiary education"
[3] "Upper secondary or post-secondary non-tertiary education"
sort(unique(education_levels_three$STATISTICAL_OPERATION))
[1] "OBS" "SE"
I want to use the observed values and not the standard error values
at this time.
phs_obs <- phs %>%
filter(educ)
Error in `filter()`:
ℹ In argument: `educ`.
Caused by error:
! object 'educ' not found
Run `]8;;x-r-run:rlang::last_trace()rlang::last_trace()]8;;` to see where the error occurred.
el_third <- education_levels_three %>%
filter(Sex == "Total") %>%
filter(Age == "From 25 to 64 years") %>%
filter(TIME_PERIOD == 2010) %>%
filter(OBS_STATUS == "A") %>%
filter(REF_AREA == "AUT") %>%
filter(STATISTICAL_OPERATION == "OBS")
el_third
NA
el_secondary <- education_levels_defined %>%
filter(Sex == "Total") %>%
filter(Age == "From 25 to 64 years") %>%
filter(TIME_PERIOD == 2010) %>%
filter(OBS_STATUS == "A") %>%
filter(REF_AREA == "AUT")
el_secondary
NA
# sort(unique(el_once$OBS_VALUE))
# sort(unique(el_once$`Educational attainment level`))
# sort(unique(el_secondary$`Educational attainment level`))
# el_once %>%
# group_by(`Educational attainment level`) %>%
# summarize(n = n())
# barplot(table(el_once$`Educational attainment level`), main = "number of observations of that education level in the data")
safety_regions <- read_csv("../data/safety_regions.csv")
safety_regions %>%
filter(TIME_PERIOD == 2010) %>%
filter(REF_AREA == "AUT") #%>%
# filter(Sex == "Total") %>%
# filter(Age == "15 years or over")
wellbeing_social %>%
filter(TIME_PERIOD == 2022) %>%
filter(REF_AREA == "AUT") #%>%
# filter(Unit)
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